Economic pressure from health care reform paired with steady increases in demand for hospital services are forcing hospitals to do more with less. This places emphasis on efficiently managing all resources used to care for patients throughout their hospital stay. This includes effectively managing patient flow by minimizing patient waiting (i.e., non-value added time), reducing patients' length-of-stay and consequently improving patient access. There are several challenges to managing patient flow through an individual unit or entire hospital, and these challenges mainly stem from uncertainty (i.e., stochastic variability) in the demand for services over time.
A Pediatric Intensive Care Unit (PICU) is a specialized hospital unit or facility designed to treat children with a wide variety of life-threatening illnesses or injuries. A significant portion of PICU admissions are from trauma-related injuries which are a leading cause of death among children in the United States. Severely ill and injured children treated in large tertiary care PICUs have significantly better survival rates compared to children treated in non-tertiary care facilities. Improved outcomes associated with PICU care have been attributed to specialized services and staff (e.g., intensivists) and their increased use of advanced technologies and therapeutic modalities. Despite the benefits of tertiary PICUs, access remains poor. PICU facilities, which are only available in 9% of United States counties, have been disproportionally affected by the on-going nursing shortage further limiting access. Thus, it is critical that each PICU across the country operate efficiently, optimize scarce nursing resources, and provide timely care when children are in need.
Individual PICUs must manage patient flow efficiently to balance competing interests of providing consistently accessible services and maintaining financial viability (i.e., avoiding idle resources). Challenges arise from uncertainty and complexity caused by; (1) stochastic variability in demand for resources, (2) nurse staffing constraints, and (3) deficiencies in communication and coordination across the hospital system. An exemplary PICU rejects 20% of patient referrals, transfers out 14% of emergency patients and operates at only 77% average bed occupancy.
While many children within the hospital system are not rejected, they do experience delays in obtaining access. Pediatric surgery patients held post-surgery create Operating Room (OR) delays. Delays are common and inconvenience the patient, surgical team and incur heavy costs for OR idle time and staff overtime. Surgeries may be cancelled if it is anticipated that PICU resources are unavailable, however this is a rare occurrence (<2%). Children in the ED have prolonged stays and treatment areas are not always optimal. Delays in the ED for critically ill adults have been demonstrated to impact the progression of organ failure and significantly increase mortality rate. Children being treated in other children's hospital inpatient units (i.e., floors) may experience deterioration in clinical condition, requiring critical care services.
When PICU access is blocked, these patients must wait in their current units. Occasionally, children are forced to wait overnight in each of these sub-optimal care areas. Blocking PICU admissions obstructs patient flow at various locations within the children's hospital system. Conversely, congestion within the hospital impedes flow through the PICU. The majority of patients (84%) are transferred to other units within the hospital to receive lower levels of care before being discharged. A common barrier to transferring patients out is lack of available beds in downstream units. These patients awaiting transition consume valuable PICU resources that are likely to be more beneficial to patients awaiting access or patients turned away.
Daily PICU management functions involve; (1) ensuring appropriate staffing levels, (2) ensuring that patient needs for equipment and services are met, (3) decisions to accept/reject direct admits, (3) decisions to transfer ED patients to other facilities, and (4) decisions to cancel surgeries. Each of these functions necessitates projecting bed census at future times.
The proven benefits of pediatric critical care have led to increases in demand and subspecialty expansion. Nationwide increases in demand are possibly due to the rise in the number of children surviving from injuries or chronic illness as a result of advances in medicine. Expansion has partially resulted from a shift in pediatric bed distribution toward more high acuity PICU beds and fewer floor beds. However, the effects of capacity expansion have been mitigated by the on-going nursing shortage, which is expected to worsen. PICUs have been disproportionally effected by the rapid decline of young nurses (i.e., under age of 30) who are historically attracted to intensive care work.
An exemplary PICU has 26 beds and admits ˜1650 patients annually with, 39% coming from operating rooms (OR), 26% from the pediatric emergency department (ED), 18% from children's hospital unit transfers, and 17% from referrals from external health care facilities (i.e., direct admits). Most PICU arrivals from the OR (97%) are selectively scheduled weeks in advance. Thus, OR patients are considered deterministic arrivals (i.e., known prior to 72 hours in advance). Patients admitted from other sources (61%) arrive naturally (i.e., unknown) and are considered stochastic. Stochastic arrivals from external health care facilities (i.e., direct admits) and the ED may be rejected. The PICU currently rejects 20% of direct admits and transports 14% of critically ill ED patients to other facilities because of lack of available resources.
Delays in the ED for critically ill adults have been demonstrated to impact the progression of organ failure and significantly increase mortality rate. These children may also be transported to external health care facilities, if PICU resources are unavailable. This is undesirable because it delays care, inconveniences the child and family, and incurs transportation costs. Children being treated in other children's hospital inpatient units (i.e., floors) may experience deterioration in clinical condition, requiring critical care services. When PICU access is blocked, these patients must wait in their current units. Occasionally, children are forced to wait overnight in each of these sub-optimal care areas. Blocking PICU admissions obstructs patient flow at various locations within the children's hospital system. Conversely, congestion within the hospital impedes flow through the PICU. The majority of patients (84%) are transferred to other units within the hospital to receive lower levels of care before being discharged. A common barrier to transferring patients out is lack of available beds in downstream units. These patients awaiting transition consume valuable PICU resources that are likely to be more beneficial to patients awaiting access or patients turned away.
Currently, nursing and bed projections are made using human intuition supplemented by standard status reports of PICU census and daily OR schedule, available each morning. Accurate projections are difficult in the stochastically variable PICU environment where the average patient census is 20 (i.e., 77% occupancy) with a 95% confidence interval of 13 to 25 children over 1-year. In addition, lack of updated PICU patient information and system component information further challenge managers' ability to project.
Models forecasting occupancy, patient arrivals, discharges, and other unit specific operational metrics have been developed for EDs and entire hospitals. Methods used within these studies include several variations of; autoregressive moving average (ARMA) models, exponential smoothing models, Poisson regression models, neural network models and discrete event simulation models. Models used to predict occupancy either use an ARMA type model on occupancy data or develop a structured model of components to forecast inflow (i.e., arrivals) and outflow (i.e., length-of-stay) independently. However, this assumption of independence may be flawed.
Inflow models employ a single above mentioned method to model arrival counts at discrete time intervals. Outflow models are most commonly comprised of length-of-stay prediction models; however there has been application of a model to predict discharge counts over discrete time intervals. Members of the proposed research team have applied several survival analysis methods to predict patient length-of-stay as function of patient, hospital, and environmental factors. Developing a forecasting model that is applicable in real-time poses a different set of intellectual and practical challenges, which have been addressed by only two previous studies. One study developed a discrete event simulation model to forecast measures of ED crowding and performed a prospective, real-time evaluation recently accepted for publication. Another study, describes an occupancy forecasting model for an entire hospital comprised of inflow and outflow components.
It would therefore be advantageous to provide a tool to forecast demand for hospital services that has the ability to inform decision making, optimize scarce resources and improve access.